CoDiNA

CoDiNA performs comparative analysis of whole-transcriptome co-expression networks to identify common (alpha), specific (gamma), and different (beta) links and nodes across multiple conditions.

Key Features:

• Systematic comparison: Enables comparison of an unlimited number of co-expression networks and transcripts simultaneously.
• Link and node categorization: Classifies links and nodes into common (alpha), different (beta), and specific (gamma) categories with sub-categories and grouping.
• Statistical normalization: Employs a statistical framework to normalize between categories and detect subtle changes in network topology.
• Cross-network applicability: Applicable to undirected networks beyond transcriptomic data, including metabolic, protein-protein interaction, ecological, and psychometric networks.
• Visualization capability: Includes a visualization component for network analysis.

Scientific Applications:

• Neurogenesis studies: Applied to identify candidate genes involved in neuronal differentiation and to assess their impact on neurogenesis gene regulatory networks.
• Clinical infectious-disease research: Used to compare whole-transcriptome co-expression networks from individuals with or without HIV and active tuberculosis (TB) to identify HIV-specific signature genes.
• Cancer research: Applied to multiple cancer transcription factor (TF) networks to identify common and distinct features across cancer types.

Methodology:

Detects and classifies links and nodes as common (alpha), different (beta), or specific (gamma) across compared networks and applies a statistical framework to normalize between these categories; applicable to undirected networks such as metabolic, protein-protein interaction, ecological, and psychometric networks.